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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields.

Niklas Frederik Schmitz1, Klaus-Robert Müller1,2,3,4,5, Stefan Chmiela1,2

  • 1Machine Learning Group, Technische Universität Berlin, 10587Berlin, Germany.

The Journal of Physical Chemistry Letters
|October 24, 2022
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Summary
This summary is machine-generated.

Machine learning models can reconstruct force fields efficiently by using algorithmic differentiation. This approach allows for automatic use of new descriptors and models, improving computational speed and enabling inclusion of more physical knowledge.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Reconstructing accurate force fields (FFs) from atomistic simulation data is computationally expensive.
  • Current machine learning (ML) models for FFs require cumbersome remodeling for new descriptors.

Purpose of the Study:

  • To develop a more data-economic and computationally efficient method for reconstructing force fields using ML.
  • To enable the automatic integration of novel descriptors and physical knowledge into ML-based FF development.

Main Methods:

  • Utilizing algorithmic differentiation within the ML modeling process.
  • Implementing symmetry and conservation laws of physics as constraints for ML models.
  • Enabling the use of higher-order information like Hessians and complex partial differential equations.

Main Results:

  • Achieved an order of magnitude increase in computational efficiency for ML-based FF reconstruction.
  • Enabled fully automatic usage of novel descriptors and models.
  • Facilitated the inclusion of advanced physical knowledge beyond standard FF domains.

Conclusions:

  • Algorithmic differentiation offers a paradigm shift for efficient and versatile ML-based force field development.
  • The proposed method significantly reduces the cost and complexity of creating accurate FFs.
  • This approach has broad applicability for enhancing ML models in various scientific domains.